HRM.
Chapter 5-HR Demand

Chapter 5-HR Demand - Chapter 5 Determining HR Demand...

• 7
• 100% (1) 1 out of 1 people found this document helpful

This preview shows pages 1–3. Sign up to view the full content.

Chapter 5: Determining HR Demand Methods of Forecasting HR demand: future needs for the firm’s skill requirements, types of jobs, and number of positions that must be filled for the firm to implement. Demand is determined by the strategic and operational requirements of the firm or business unit Quantitative Method A. Trend Analysis: forecasting method that extrapolates from historical organizational indices Ex. Single indices include sales, sales per employee, etc. Ratio Analysis: analyzing the relationship between an operational index and the number of employees required Commonly used by many organizations Sales level is the most common index used by organizations Other indices include: the number of units produced, the number of clients serviced, and the production hours Some organizations use trend analysis to ascertain demand requirements for direct and indirect labour 5 Steps to conducting an effective trend analysis: Select the appropriate business/operational index Ex. Sales level Operational index must be known to have a direct influence on the organizational demand for labour and is subjected to future forecasting as a result of the normal business planning process Track the operational index over time It is necessary to go back in time for at least the four or five most recent year to record the quantitative or numerical levels of the index over time Track the workforce size over time Ex. Total number of employees or amount of direct and indirect labour Calculate the average ration of the operational index to the workforce size Employee requirement ratio: divide the level of sales for each year of historical data by the number of employees required to product that year’s level of sales Calculate the forecasted demand for labour

This preview has intentionally blurred sections. Sign up to view the full version.

Divide the annual forecast for the operational index by the average employee requirement ratio for each future year in order to arrive at forecasted annual demand for labour Ex. Obtain future sales forecast figures for the next 5 years, divide the lvl of sales by the avg employee requirement ratio to obtain the forecasted numerical demand for labour for each future year B. Time Series Model Use past data to predict future demand Weighted moving average: places more importance on recent demand data To capture seasonality or trends in demand, exponential smoothing is used Exponential smoothing also uses all past demand data, and places priority on recent demand data (even more than weighted moving avg) Usually incorporates only the relationship between a single business variable and demand for labour (workforce size) Multivariate regression or other similar modeling/programming models used to analyze more comprehensive analysis such as level of UE.
This is the end of the preview. Sign up to access the rest of the document.
• Winter '13
• MelanieChaparian
• Forecasting, operational index, employee requirement ratio

{[ snackBarMessage ]}

What students are saying

• As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

Kiran Temple University Fox School of Business ‘17, Course Hero Intern

• I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

Dana University of Pennsylvania ‘17, Course Hero Intern

• The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

Jill Tulane University ‘16, Course Hero Intern